2 code implementations • LREC 2022 • Tosin P. Adewumi, Roshanak Vadoodi, Aparajita Tripathy, Konstantina Nikolaidou, Foteini Liwicki, Marcus Liwicki
The challenges with NLP systems with regards to tasks such as Machine Translation (MT), word sense disambiguation (WSD) and information retrieval make it imperative to have a labelled idioms dataset with classes such as it is in this work.
1 code implementation • 15 Nov 2020 • Tosin P. Adewumi, Foteini Liwicki, Marcus Liwicki
The major contributions of this work include the empirical establishment of a better performance for Yoruba embeddings from undiacritized (normalized) dataset and provision of new analogy sets for evaluation.
no code implementations • 6 Nov 2020 • Tosin P. Adewumi, Foteini Liwicki, Marcus Liwicki
In this work, we show that the difference in performance of embeddings from differently sourced data for a given language can be due to other factors besides data size.
1 code implementation • 23 Jul 2020 • Tosin P. Adewumi, Foteini Liwicki, Marcus Liwicki
To achieve a good network performance in natural language processing (NLP) downstream tasks, several factors play important roles: dataset size, the right hyper-parameters, and well-trained embeddings.
2 code implementations • 23 Mar 2020 • Tosin P. Adewumi, Foteini Liwicki, Marcus Liwicki
However, wrong combination of hyper-parameters can produce poor quality vectors.